PROJECT SUMMARY/ABSTRACT Annually, more than 200,000 patients die in U.S. hospitals from cardiac arrest1 and over 130,000 patients inpatients deaths are attributed to sepsis.2 These deaths are preventable if patients who are at risk are detected earlier. Our prior work found that nursing documentation within electronic health records (EHRs) contains information that could contribute to early detection and treatment, but these data are not being analyzed and exposed by EHRs to clinicians to initiate interventions quickly enough to save patients.3?6 We defined a new source of predictive data by analyzing the frequency and types of nursing documentation that indicated nurses' increased surveillance and level of concern for a patient. These data documented in the 48 hours preceding a cardiac arrest and hospital mortality were predictive of the event.3 While clinicians strive to provide the best care, there is a systematic problem within hospital settings of non-optimal communication between nurses and doctors leading to delays in care for patient at risk.6?8,9 Well-designed and tested EHRs are able to trend data and support communication and decision making, but too often fall short of these goals and actually increase clinician cognitive load through fragmented information displays, ?note bloat?, and information overload.10 Substitutable Medical Applications & Reusable Technologies (SMARTapps) using Fast Health Interoperability Resource (FHIR) standard allow for open sharing and use of innovations across EHR systems. The aim of this project is to design and evaluate a SMARTapp on FHIR used across two large academic medical centers that exposes to physicians and nurses our new predictive data source from nursing documentation to increase care team situational awareness of at risk patients to decrease preventable adverse outcomes. The SMARTapp we will design and evaluate is the Communicating Narrative Concerns Entered by RNs (CONCERN) Clinical Decision Support (CDS) system. This will be integrated at four hospitals part of two health systems, Brigham and Women's Hospital (BWH) and Newton Wellesley Hospital (NWH), part of Partners Healthcare System (PHS) in Boston, and NewYork-Presbyterian Hospital-Columbia University Medical Center (NYP-CUMC) and The Allen Hospital, part of New York Presbyterian Health System (NYP) in New York. Specifically, we will: 1) validate desired thresholds for the CONCERN SMARTapp, 2) integrate the CONCERN SMARTapp for early warning of risky patient states within CDS tools, 3) evaluate the CONCERN SMARTapp on primary outcomes of in-hospital mortality and length of stay and secondary outcomes of cardiac arrest, unanticipated transfers to the intensive care unit, and 30-day hospital readmission rates. The methods we will use include: data-mining and natural language processing, factorial design surveys, simulation testing for evaluating team-based situational awareness, and outcomes evaluation in the Medical Intensive Care Units and Acute Care Units (non-ICU) at our study sites.